基于多维特征分析的社交网络意见领袖挖掘

doi:10.3969/j.issn.0372-2112.2016.04.021

电子学报 ›› 2016, Vol. 44 ›› Issue (4): 898-905.DOI: 10.3969/j.issn.0372-2112.2016.04.021

基于多维特征分析的社交网络意见领袖挖掘

曹玖新, 陈高君, 吴江林, 刘波, 周涛, 胥帅, 朱子青

1. 东南大学计算机科学与工程学院, 江苏南京 210096;
2. 东南大学计算机网络和信息集成教育部重点实验室, 江苏南京 210096

收稿日期:2014-10-16 修回日期:2015-06-12 出版日期:2016-04-25
- 通讯作者:
- 曹玖新
- 作者简介:
- 陈高君 男,1988年生于河南漯河,东南大学硕士生,主要研究领域为社会计算;吴江林 男,1988年生于江苏南通市,东南大学硕士生,主要研究领域为社会计算.
- 基金资助:
- 国家863高技术研究发展计划 (No.2013AA013503); 东南大学计算机网络和信息集成教育部重点实验室基金 (No.93k-9); 国家973重点基础研究发展计划 (No.2010CB328104); 国家自然科学基金 (No.61272531,No.61202449,No.61272054,No.61370207,No.61370208,No.61300024,No.61320106007,No.61472081); 高等学校博士点学科专项科研基金 (No.2011009213002); 江苏省科技计划基金 (No.SBY2014021039-10); 江苏省网络与信息安全重点实验室基金 (No.BM2003201)

Multi-Feature Based Opinion Leader Mining in Social Networks

CAO Jiu-xin, CHEN Gao-jun, WU Jiang-lin, LIU Bo, ZHOU Tao, XU Shuai, ZHU Zi-qing

1. School of Computer Science and Engineering, Southeast University, Nanjing, Jiangsu 210096, China;
2. Key Laboratory of Computer Network and Information Integration (Ministry of Education), Southeast University, Nanjing, Jiangsu 210096, China

Received:2014-10-16 Revised:2015-06-12 Online:2016-04-25 Published:2016-04-25

摘要/Abstract

摘要：

在社交网络中进行意见领袖的挖掘对信息传播与演化的深度分析、舆情监控和引导具有重要意义,本文综合结构特征、行为特征和用户的情感特征对意见领袖节点挖掘问题进行研究.本文首先对微博真实文本数据进行话题识别得到主题社区,在主题社区中基于用户节点之间的关注关系构建交互网络拓扑.然后分别从结构、行为和情感三个维度对用户的影响力进行度量.最后,分析用户在主题社区中的影响力分布与传播规律,提出意见领袖识别算法MFP(Multi-Feature PageRank).实验表明,该算法可有效地挖掘潜在的意见领袖节点,能够获得较高的支持率.

关键词: 社交网络, 话题, 情感分析, 意见领袖

Abstract:

Mining opinion leaders in social network is important for analysis of information dissemination and evolution of public opinion.This paper conducts the study on this problem considering structural features,behavior and emotional characteristics comprehensively.Firstly,we extract topics from micro-blogging texts,and get user communities according to the topic division,and an interactive network topology of topic community is built with the following relationships.Then,three kinds of user feature are gained from different aspect:network structure,user behavior and user sentiment.Finally,according to the analysis of users' influence distribution,opinion leaders mining algorithm MFP (Multi-Feature PageRank) is proposed.Experiments show that the algorithm can obtain the potential opinion leader nodes effectively,and have a good performance in support rate from other user nodes.

Key words: social network, topic, sentiment analysis, opinion leader

中图分类号:

TP393

曹玖新, 陈高君, 吴江林, 刘波, 周涛, 胥帅, 朱子青. 基于多维特征分析的社交网络意见领袖挖掘[J]. 电子学报, 2016, 44(4): 898-905.

CAO Jiu-xin, CHEN Gao-jun, WU Jiang-lin, LIU Bo, ZHOU Tao, XU Shuai, ZHU Zi-qing. Multi-Feature Based Opinion Leader Mining in Social Networks[J]. Acta Electronica Sinica, 2016, 44(4): 898-905.

参考文献

[1] Ellison N B.Social network sites:Definition,history,and scholarship[J].Journal of Computer-Mediated Communication,2007,13(1):210-230.
[2] Kleinberg J M.Authoritative sources in a hyperlinked environment[J].Journal of the ACM (JACM),1999,46(5):604-632.
[3] Brin S,Page L.The anatomy of a large-scale hypertextual Web search engine[J].ComputerNetworks and ISDN Systems,1998,30(1):107-117.
[4] Kleinberg J M.Hubs,authorities,and communities[J].ACM Computing Surveys (CSUR),1999,31(4es):5.
[5] Zhai Z,Xu H,Jia P.Identifying opinion leaders in BBS[A].IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology [C].IEEE,2008.398-401.
[6] Amit G,Francesco B,Laks V S L.Discovering leaders from community actions[A].International Conference on Information and Knowledge Management (CIKM) [C].California,USA,2008.499-508.
[7] Tsai M F,Tzeng C W,Lin Z L,et al.Discovering leaders from social network by action cascade[J].Social Network Analysis and Mining,2014,4(1):1-10.
[8] Xiaodan S,Yun C,Koji H,et al.Identifying opinion leaders in the Blogosphere[A].Proceedings of the Sixteenth ACM Conference on Information and Knowledge Management[C].New York,USA,2007.971-974.
[9] Li Y,Ma S,Zhang Y,et al.An improved mix framework for opinion leader identification in online learning communities[J].Knowledge-Based Systems,2013,43:43-51.
[10] Zhou H,Zeng D,Zhang C.Finding leaders from opinion networks[A].Intelligence and Security Informatics[C].Dallas,USA,2009.266-268.
[11] Liu X,Wang Y,Li Y,et al.Identifying topic experts and topic communities in the blogspace[A].Database Systems for Advanced Applications[M].Berlin Heidelberg:Springer,2011.68-77.
[12] 夏霖.BBS中组织拓扑结构研究和意见领袖识别[D].武汉:华中科技大学,2011. Xia Lin.Topology structure research and opinion leader identifying in BBS [D].Wuhan:Huazhong University of Science and Technology,2011.(in Chinese)
[13] Duan Jiangjiao,Jianping Zeng,Banghui Luo.Identification of opinion leaders based on user clustering and sentiment analysis[A].2014 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT)[C].IEEE,2014.vol 1.
[14] Song K,Wang D,Feng S,et al.Detecting opinion leader dynamically in Chinese news comments[A].Web-Age Information Management[M].Berlin Heidelberg:Springer,2012.197-209.
[15] NTUSD.[EB/OL].http://www.datatang.com/data/11837.
[16] HowNet.[EB/OL].HowNet's Home Page.http://www.keenage.com.
[17] 同义词词林扩展版.[EB/OL].http://www.ir-lab.org.

基于多维特征分析的社交网络意见领袖挖掘

Multi-Feature Based Opinion Leader Mining in Social Networks

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

[1]	王飞扬, 冀鹏欣, 孙笠, 危倩, 李根, 张忠宝. 一种基于深度学习的动态社交网络用户对齐方法[J]. 电子学报, 2022, 50(8): 1925-1936.
[2]	高鹏, 柴鹏翔, 郎俊. 基于0-1背包算法的社交网络行为隐写术[J]. 电子学报, 2022, 50(3): 753-758.
[3]	王志晓, 张磊, 孙成成, 芮晓彬, 黄珍珍, 张孙贤. 基于社区划分与连边逆序放回的网络分解算法[J]. 电子学报, 2022, 50(3): 540-547.
[4]	宋宇琦, 高旻, 李骏东, 荣文戈, 熊庆宇. 网络欺凌检测综述[J]. 电子学报, 2020, 48(6): 1220-1229.
[5]	肖云鹏, 孙华超, 戴天骥, 李茜, 李暾. 一种基于云模型的社交网络推荐系统评分预测方法[J]. 电子学报, 2018, 46(7): 1762-1767.
[6]	肖云鹏, 刘晏驰, 刘红, 刘媛妮. 一种基于动态角色标识和张量分解的推荐模型[J]. 电子学报, 2018, 46(3): 569-574.
[7]	于建业, 王元卓, 靳小龙, 程学旗. 基于社交演化博弈的社交网络用户信息分享行为演化分析[J]. 电子学报, 2018, 46(1): 223-229.
[8]	柏文言, 张闯, 徐克付, 张志明. 一种融合用户关系的自适应微博话题跟踪方法[J]. 电子学报, 2017, 45(6): 1375-1381.
[9]	陈杰, 刘学军, 李斌, 章玮. 一种基于用户动态兴趣和社交网络的微博推荐方法[J]. 电子学报, 2017, 45(4): 898-905.
[10]	肖云鹏, 刘瀚松, 刘宴兵. 一种基于二部图和节点角色划分的社交网络推荐方案[J]. 电子学报, 2017, 45(10): 2425-2433.
[11]	王锐, 吴玲玲, 石川, 吴斌. 基于社团结构的链接预测和属性推断联合解决方法[J]. 电子学报, 2016, 44(9): 2062-2067.
[12]	黄发良, 李超雄, 元昌安, 汪焱, 姚志强. 基于TSCM模型的网络短文本情感挖掘[J]. 电子学报, 2016, 44(8): 1887-1891.
[13]	许文文, 时鹏, 于留宝, 胡长军. 一种在线社交网络的自适应观点引导模型及实现[J]. 电子学报, 2016, 44(7): 1714-1720.
[14]	刘全超, 黄河燕, 冯冲. 面向中文微博的评价对象与评价词语联合抽取[J]. 电子学报, 2016, 44(7): 1662-1670.
[15]	于岩, 陈鸿昶, 于洪涛. 基于霍克斯过程的社交网络用户关系强度模型[J]. 电子学报, 2016, 44(6): 1362-1368.